Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations39066
Missing cells61448
Missing cells (%)4.5%
Total size in memory10.4 MiB
Average record size in memory280.0 B

Variable types

Numeric10
Text25

Alerts

COMPANY_LINKEDIN_URL has 7728 (19.8%) missing valuesMissing
ENDDATE has 7322 (18.7%) missing valuesMissing
DESCRIPTION has 16102 (41.2%) missing valuesMissing
RCID has 5016 (12.8%) missing valuesMissing
COMPANY_NAME has 5022 (12.9%) missing valuesMissing
ULTIMATE_PARENT_RCID has 5017 (12.8%) missing valuesMissing
ULTIMATE_PARENT_COMPANY_NAME has 5023 (12.9%) missing valuesMissing
RICS_K50 has 5020 (12.9%) missing valuesMissing
RICS_K400 has 5020 (12.9%) missing valuesMissing
WEIGHT is highly skewed (γ1 = 31.0129104)Skewed
POSITION_ID has unique valuesUnique
PRESTIGE has 682 (1.7%) zerosZeros

Reproduction

Analysis started2025-09-30 07:03:37.617512
Analysis finished2025-09-30 07:03:43.273267
Duration5.66 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

USER_ID
Real number (ℝ)

Distinct4655
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547509440.7
Minimum1241390
Maximum2225886059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:43.767848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1241390
5-th percentile38208293
Q1211330625
median427319967
Q3657391480
95-th percentile2043478202
Maximum2225886059
Range2224644669
Interquartile range (IQR)446060855

Descriptive statistics

Standard deviation519583579.9
Coefficient of variation (CV)0.948994741
Kurtosis3.427904804
Mean547509440.7
Median Absolute Deviation (MAD)224396949
Skewness1.936478357
Sum2.138900381 × 1013
Variance2.699670965 × 1017
MonotonicityNot monotonic
2025-09-30T03:03:43.959990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1054172296143
 
0.4%
2184947765134
 
0.3%
41823953265
 
0.2%
17734848656
 
0.1%
86935864054
 
0.1%
48672652553
 
0.1%
3614338853
 
0.1%
52222125953
 
0.1%
99153642250
 
0.1%
1692746946
 
0.1%
Other values (4645)38359
98.2%
ValueCountFrequency (%)
12413908
< 0.1%
12498868
< 0.1%
12843285
< 0.1%
14902445
< 0.1%
151135911
< 0.1%
ValueCountFrequency (%)
22258860594
< 0.1%
22257476447
< 0.1%
22255996365
< 0.1%
22252510207
< 0.1%
22241732213
< 0.1%

POSITION_ID
Real number (ℝ)

Unique 

Distinct39066
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.913436742 × 1016
Minimum-9.223262036 × 1018
Maximum9.2228834 × 1018
Zeros0
Zeros (%)0.0%
Negative19451
Negative (%)49.8%
Memory size305.3 KiB
2025-09-30T03:03:44.177576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.223262036 × 1018
5-th percentile-8.270419886 × 1018
Q1-4.539560736 × 1018
median4.507716992 × 1016
Q34.607073719 × 1018
95-th percentile8.293585981 × 1018
Maximum9.2228834 × 1018
Range-5.986376959 × 1014
Interquartile range (IQR)9.146634454 × 1018

Descriptive statistics

Standard deviation5.307580319 × 1018
Coefficient of variation (CV)277.3846766
Kurtosis-1.191005313
Mean1.913436742 × 1016
Median Absolute Deviation (MAD)4.57511961 × 1018
Skewness-0.002329015401
Sum-8.813309353 × 1018
Variance2.817040884 × 1037
MonotonicityNot monotonic
2025-09-30T03:03:44.411893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.150475116 × 10171
 
< 0.1%
1.260022533 × 10181
 
< 0.1%
-4.871772573 × 10181
 
< 0.1%
-4.939933557 × 10181
 
< 0.1%
5.131730077 × 10181
 
< 0.1%
-2.865002515 × 10181
 
< 0.1%
-6.964772228 × 10181
 
< 0.1%
-1.39503025 × 10181
 
< 0.1%
-5.757123625 × 10181
 
< 0.1%
2.168781311 × 10181
 
< 0.1%
Other values (39056)39056
> 99.9%
ValueCountFrequency (%)
-9.223262036 × 10181
< 0.1%
-9.222571923 × 10181
< 0.1%
-9.222324339 × 10181
< 0.1%
-9.22026933 × 10181
< 0.1%
-9.220119657 × 10181
< 0.1%
ValueCountFrequency (%)
9.2228834 × 10181
< 0.1%
9.222838336 × 10181
< 0.1%
9.222289811 × 10181
< 0.1%
9.222155478 × 10181
< 0.1%
9.222031202 × 10181
< 0.1%
Distinct22637
Distinct (%)58.0%
Missing27
Missing (%)0.1%
Memory size305.3 KiB
2025-09-30T03:03:44.830228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length101
Median length87
Mean length21.20087605
Min length1

Characters and Unicode

Total characters827661
Distinct characters176
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17831 ?
Unique (%)45.7%

Sample

1st rowPrimix Solutions
2nd rowTime Inc.
3rd rowValassis Marketing Solutions
4th rowPSONE Art Education
5th rowMacy's
ValueCountFrequency (%)
of4364
 
3.6%
university3632
 
3.0%
2685
 
2.2%
the2381
 
2.0%
new1554
 
1.3%
inc1462
 
1.2%
school1307
 
1.1%
york1180
 
1.0%
center1073
 
0.9%
college1041
 
0.9%
Other values (19731)99154
82.7%
2025-09-30T03:03:45.425951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80853
 
9.8%
e71500
 
8.6%
i52214
 
6.3%
n51210
 
6.2%
a50944
 
6.2%
o50555
 
6.1%
t46591
 
5.6%
r46048
 
5.6%
s33780
 
4.1%
l30782
 
3.7%
Other values (166)313184
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)827661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
80853
 
9.8%
e71500
 
8.6%
i52214
 
6.3%
n51210
 
6.2%
a50944
 
6.2%
o50555
 
6.1%
t46591
 
5.6%
r46048
 
5.6%
s33780
 
4.1%
l30782
 
3.7%
Other values (166)313184
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)827661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
80853
 
9.8%
e71500
 
8.6%
i52214
 
6.3%
n51210
 
6.2%
a50944
 
6.2%
o50555
 
6.1%
t46591
 
5.6%
r46048
 
5.6%
s33780
 
4.1%
l30782
 
3.7%
Other values (166)313184
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)827661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
80853
 
9.8%
e71500
 
8.6%
i52214
 
6.3%
n51210
 
6.2%
a50944
 
6.2%
o50555
 
6.1%
t46591
 
5.6%
r46048
 
5.6%
s33780
 
4.1%
l30782
 
3.7%
Other values (166)313184
37.8%

COMPANY_LINKEDIN_URL
Text

Missing 

Distinct15385
Distinct (%)49.1%
Missing7728
Missing (%)19.8%
Memory size305.3 KiB
2025-09-30T03:03:45.801722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length326
Median length98
Mean length38.10587785
Min length23

Characters and Unicode

Total characters1194162
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10973 ?
Unique (%)35.0%

Sample

1st rowlinkedin.com/company/time-inc-
2nd rowlinkedin.com/company/valassis
3rd rowlinkedin.com/company/world-link-education-ltd
4th rowlinkedin.com/company/macy
5th rowlinkedin.com/company/freelance-self-employed_4
ValueCountFrequency (%)
linkedin.com/company/ibm156
 
0.5%
linkedin.com/company/goldman-sachs143
 
0.5%
linkedin.com/company/columbia-university133
 
0.4%
linkedin.com/company/memorial-sloan-kettering-cancer-center129
 
0.4%
linkedin.com/company/pwc125
 
0.4%
linkedin.com/company/deloitte119
 
0.4%
linkedin.com/company/citi107
 
0.3%
linkedin.com/company/ernstandyoung100
 
0.3%
linkedin.com/company/morgan-stanley99
 
0.3%
linkedin.com/company/jpmorganchase98
 
0.3%
Other values (15375)30129
96.1%
2025-09-30T03:03:46.356835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n131875
 
11.0%
i102014
 
8.5%
o99549
 
8.3%
c88365
 
7.4%
e81576
 
6.8%
m76618
 
6.4%
a70764
 
5.9%
/62679
 
5.2%
l58576
 
4.9%
d44153
 
3.7%
Other values (40)377993
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1194162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n131875
 
11.0%
i102014
 
8.5%
o99549
 
8.3%
c88365
 
7.4%
e81576
 
6.8%
m76618
 
6.4%
a70764
 
5.9%
/62679
 
5.2%
l58576
 
4.9%
d44153
 
3.7%
Other values (40)377993
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1194162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n131875
 
11.0%
i102014
 
8.5%
o99549
 
8.3%
c88365
 
7.4%
e81576
 
6.8%
m76618
 
6.4%
a70764
 
5.9%
/62679
 
5.2%
l58576
 
4.9%
d44153
 
3.7%
Other values (40)377993
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1194162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n131875
 
11.0%
i102014
 
8.5%
o99549
 
8.3%
c88365
 
7.4%
e81576
 
6.8%
m76618
 
6.4%
a70764
 
5.9%
/62679
 
5.2%
l58576
 
4.9%
d44153
 
3.7%
Other values (40)377993
31.7%
Distinct22493
Distinct (%)57.6%
Missing25
Missing (%)0.1%
Memory size305.3 KiB
2025-09-30T03:03:46.739852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length99
Median length84
Mean length20.83314977
Min length2

Characters and Unicode

Total characters813347
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17647 ?
Unique (%)45.2%

Sample

1st rowprimix solutions
2nd rowtime inc
3rd rowvalassis marketing solutions
4th rowpsone art education
5th rowmacy s
ValueCountFrequency (%)
of4369
 
3.6%
university3673
 
3.0%
the2387
 
2.0%
new1585
 
1.3%
inc1470
 
1.2%
school1311
 
1.1%
york1215
 
1.0%
s1100
 
0.9%
center1081
 
0.9%
college1050
 
0.9%
Other values (19006)101304
84.0%
2025-09-30T03:03:47.387562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81482
 
10.0%
e76545
 
9.4%
a60406
 
7.4%
i59500
 
7.3%
n57910
 
7.1%
o53589
 
6.6%
t53538
 
6.6%
r50910
 
6.3%
s47403
 
5.8%
c38744
 
4.8%
Other values (113)233320
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)813347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81482
 
10.0%
e76545
 
9.4%
a60406
 
7.4%
i59500
 
7.3%
n57910
 
7.1%
o53589
 
6.6%
t53538
 
6.6%
r50910
 
6.3%
s47403
 
5.8%
c38744
 
4.8%
Other values (113)233320
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)813347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81482
 
10.0%
e76545
 
9.4%
a60406
 
7.4%
i59500
 
7.3%
n57910
 
7.1%
o53589
 
6.6%
t53538
 
6.6%
r50910
 
6.3%
s47403
 
5.8%
c38744
 
4.8%
Other values (113)233320
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)813347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81482
 
10.0%
e76545
 
9.4%
a60406
 
7.4%
i59500
 
7.3%
n57910
 
7.1%
o53589
 
6.6%
t53538
 
6.6%
r50910
 
6.3%
s47403
 
5.8%
c38744
 
4.8%
Other values (113)233320
28.7%
Distinct5405
Distinct (%)13.8%
Missing1
Missing (%)< 0.1%
Memory size305.3 KiB
2025-09-30T03:03:47.829757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length94
Median length88
Mean length26.48777678
Min length1

Characters and Unicode

Total characters1034745
Distinct characters152
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3537 ?
Unique (%)9.1%

Sample

1st rowNew York City Metropolitan Area, United States
2nd rowNew York, NY
3rd rowNew York City Metropolitan Area, United States
4th rowNew York City Metropolitan Area
5th rowGreater New York City Area
ValueCountFrequency (%)
new36178
21.1%
york34553
20.1%
united16126
9.4%
states15823
9.2%
area10218
 
6.0%
city9968
 
5.8%
metropolitan6533
 
3.8%
greater3764
 
2.2%
ny2670
 
1.6%
jersey1295
 
0.8%
Other values (4098)34388
20.0%
2025-09-30T03:03:48.459555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132482
 
12.8%
e110987
 
10.7%
t86954
 
8.4%
r72617
 
7.0%
o64198
 
6.2%
a59574
 
5.8%
i48611
 
4.7%
n42163
 
4.1%
N40573
 
3.9%
,40463
 
3.9%
Other values (142)336123
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1034745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132482
 
12.8%
e110987
 
10.7%
t86954
 
8.4%
r72617
 
7.0%
o64198
 
6.2%
a59574
 
5.8%
i48611
 
4.7%
n42163
 
4.1%
N40573
 
3.9%
,40463
 
3.9%
Other values (142)336123
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1034745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132482
 
12.8%
e110987
 
10.7%
t86954
 
8.4%
r72617
 
7.0%
o64198
 
6.2%
a59574
 
5.8%
i48611
 
4.7%
n42163
 
4.1%
N40573
 
3.9%
,40463
 
3.9%
Other values (142)336123
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1034745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132482
 
12.8%
e110987
 
10.7%
t86954
 
8.4%
r72617
 
7.0%
o64198
 
6.2%
a59574
 
5.8%
i48611
 
4.7%
n42163
 
4.1%
N40573
 
3.9%
,40463
 
3.9%
Other values (142)336123
32.5%

REGION
Text

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:48.671048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length33
Median length16
Mean length15.72270005
Min length5

Characters and Unicode

Total characters614223
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern America
2nd rowNorthern America
3rd rowNorthern America
4th rowNorthern America
5th rowNorthern America
ValueCountFrequency (%)
northern35636
45.7%
america35548
45.6%
asia1207
 
1.5%
europe1132
 
1.5%
southern914
 
1.2%
empty837
 
1.1%
eastern566
 
0.7%
western509
 
0.7%
and255
 
0.3%
central255
 
0.3%
Other values (9)1080
 
1.4%
2025-09-30T03:03:49.039701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r110844
18.0%
e76422
12.4%
t39347
 
6.4%
a39143
 
6.4%
38873
 
6.3%
n38653
 
6.3%
o37844
 
6.2%
i37251
 
6.1%
A36991
 
6.0%
h36962
 
6.0%
Other values (18)121893
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)614223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r110844
18.0%
e76422
12.4%
t39347
 
6.4%
a39143
 
6.4%
38873
 
6.3%
n38653
 
6.3%
o37844
 
6.2%
i37251
 
6.1%
A36991
 
6.0%
h36962
 
6.0%
Other values (18)121893
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)614223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r110844
18.0%
e76422
12.4%
t39347
 
6.4%
a39143
 
6.4%
38873
 
6.3%
n38653
 
6.3%
o37844
 
6.2%
i37251
 
6.1%
A36991
 
6.0%
h36962
 
6.0%
Other values (18)121893
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)614223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r110844
18.0%
e76422
12.4%
t39347
 
6.4%
a39143
 
6.4%
38873
 
6.3%
n38653
 
6.3%
o37844
 
6.2%
i37251
 
6.1%
A36991
 
6.0%
h36962
 
6.0%
Other values (18)121893
19.8%

COUNTRY
Text

Distinct139
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:49.376642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length13
Mean length12.38214816
Min length4

Characters and Unicode

Total characters483721
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united35296
47.3%
states34942
46.8%
empty837
 
1.1%
india426
 
0.6%
kingdom313
 
0.4%
canada250
 
0.3%
china225
 
0.3%
france162
 
0.2%
south98
 
0.1%
brazil94
 
0.1%
Other values (154)2045
 
2.7%
2025-09-30T03:03:49.931733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t106634
22.0%
e72386
15.0%
a38550
 
8.0%
n37795
 
7.8%
i37381
 
7.7%
d36628
 
7.6%
35622
 
7.4%
s35533
 
7.3%
U35311
 
7.3%
S35264
 
7.3%
Other values (41)12617
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)483721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t106634
22.0%
e72386
15.0%
a38550
 
8.0%
n37795
 
7.8%
i37381
 
7.7%
d36628
 
7.6%
35622
 
7.4%
s35533
 
7.3%
U35311
 
7.3%
S35264
 
7.3%
Other values (41)12617
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)483721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t106634
22.0%
e72386
15.0%
a38550
 
8.0%
n37795
 
7.8%
i37381
 
7.7%
d36628
 
7.6%
35622
 
7.4%
s35533
 
7.3%
U35311
 
7.3%
S35264
 
7.3%
Other values (41)12617
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)483721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t106634
22.0%
e72386
15.0%
a38550
 
8.0%
n37795
 
7.8%
i37381
 
7.7%
d36628
 
7.6%
35622
 
7.4%
s35533
 
7.3%
U35311
 
7.3%
S35264
 
7.3%
Other values (41)12617
 
2.6%

STATE
Text

Distinct446
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:50.277451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length8
Mean length8.41227666
Min length3

Characters and Unicode

Total characters328634
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)0.4%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowNew York
ValueCountFrequency (%)
new27538
40.3%
york25632
37.5%
jersey1805
 
2.6%
empty1689
 
2.5%
california1102
 
1.6%
massachusetts757
 
1.1%
washington681
 
1.0%
pennsylvania587
 
0.9%
d.c579
 
0.8%
connecticut553
 
0.8%
Other values (489)7483
 
10.9%
2025-09-30T03:03:50.798673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e37963
11.6%
r32043
9.8%
o31802
9.7%
29340
8.9%
N27878
8.5%
w27696
8.4%
k25971
 
7.9%
Y25649
 
7.8%
a12722
 
3.9%
n9727
 
3.0%
Other values (47)67843
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)328634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e37963
11.6%
r32043
9.8%
o31802
9.7%
29340
8.9%
N27878
8.5%
w27696
8.4%
k25971
 
7.9%
Y25649
 
7.8%
a12722
 
3.9%
n9727
 
3.0%
Other values (47)67843
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)328634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e37963
11.6%
r32043
9.8%
o31802
9.7%
29340
8.9%
N27878
8.5%
w27696
8.4%
k25971
 
7.9%
Y25649
 
7.8%
a12722
 
3.9%
n9727
 
3.0%
Other values (47)67843
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)328634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e37963
11.6%
r32043
9.8%
o31802
9.7%
29340
8.9%
N27878
8.5%
w27696
8.4%
k25971
 
7.9%
Y25649
 
7.8%
a12722
 
3.9%
n9727
 
3.0%
Other values (47)67843
20.6%
Distinct530
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:51.255770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length53
Median length31
Mean length29.0234475
Min length5

Characters and Unicode

Total characters1133830
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)0.3%

Sample

1st rownew york city metropolitan area
2nd rownew york city metropolitan area
3rd rownew york city metropolitan area
4th rownew york city metropolitan area
5th rownew york city metropolitan area
ValueCountFrequency (%)
area37714
22.4%
metropolitan33406
19.8%
new27179
16.1%
york26746
15.9%
city24605
14.6%
nonmetropolitan4308
 
2.6%
empty1352
 
0.8%
washington690
 
0.4%
boston636
 
0.4%
san574
 
0.3%
Other values (572)11319
 
6.7%
2025-09-30T03:03:51.884525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
129463
11.4%
a124406
11.0%
o114520
10.1%
e110849
9.8%
r106973
9.4%
t106485
9.4%
n82944
7.3%
i70571
 
6.2%
y53854
 
4.7%
l43114
 
3.8%
Other values (19)190651
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1133830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
129463
11.4%
a124406
11.0%
o114520
10.1%
e110849
9.8%
r106973
9.4%
t106485
9.4%
n82944
7.3%
i70571
 
6.2%
y53854
 
4.7%
l43114
 
3.8%
Other values (19)190651
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1133830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
129463
11.4%
a124406
11.0%
o114520
10.1%
e110849
9.8%
r106973
9.4%
t106485
9.4%
n82944
7.3%
i70571
 
6.2%
y53854
 
4.7%
l43114
 
3.8%
Other values (19)190651
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1133830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
129463
11.4%
a124406
11.0%
o114520
10.1%
e110849
9.8%
r106973
9.4%
t106485
9.4%
n82944
7.3%
i70571
 
6.2%
y53854
 
4.7%
l43114
 
3.8%
Other values (19)190651
16.8%

MSA
Text

Distinct257
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:52.220537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length53
Median length53
Mean length40.68594174
Min length5

Characters and Unicode

Total characters1589437
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)0.2%

Sample

1st rowNew York-Northern New Jersey-Long Island NY-NJ-PA MSA
2nd rowNew York-Northern New Jersey-Long Island NY-NJ-PA MSA
3rd rowNew York-Northern New Jersey-Long Island NY-NJ-PA MSA
4th rowNew York-Northern New Jersey-Long Island NY-NJ-PA MSA
5th rowNew York-Northern New Jersey-Long Island NY-NJ-PA MSA
ValueCountFrequency (%)
new51470
24.6%
msa31754
15.2%
ny-nj-pa25618
12.2%
york-northern25618
12.2%
island25618
12.2%
jersey-long25618
12.2%
empty7079
 
3.4%
ca901
 
0.4%
boston-cambridge-quincy635
 
0.3%
ma-nh635
 
0.3%
Other values (355)14206
 
6.8%
2025-09-30T03:03:52.754457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170086
 
10.7%
e145193
 
9.1%
N132076
 
8.3%
-114851
 
7.2%
r109822
 
6.9%
n88239
 
5.6%
o86393
 
5.4%
A63264
 
4.0%
s55891
 
3.5%
w52351
 
3.3%
Other values (46)571271
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1589437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
170086
 
10.7%
e145193
 
9.1%
N132076
 
8.3%
-114851
 
7.2%
r109822
 
6.9%
n88239
 
5.6%
o86393
 
5.4%
A63264
 
4.0%
s55891
 
3.5%
w52351
 
3.3%
Other values (46)571271
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1589437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
170086
 
10.7%
e145193
 
9.1%
N132076
 
8.3%
-114851
 
7.2%
r109822
 
6.9%
n88239
 
5.6%
o86393
 
5.4%
A63264
 
4.0%
s55891
 
3.5%
w52351
 
3.3%
Other values (46)571271
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1589437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
170086
 
10.7%
e145193
 
9.1%
N132076
 
8.3%
-114851
 
7.2%
r109822
 
6.9%
n88239
 
5.6%
o86393
 
5.4%
A63264
 
4.0%
s55891
 
3.5%
w52351
 
3.3%
Other values (46)571271
35.9%

CITY
Text

Distinct1578
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:53.360206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length10.51018789
Min length1

Characters and Unicode

Total characters410591
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique696 ?
Unique (%)1.8%

Sample

1st rowNew York City
2nd rowNew York City
3rd rowNew York City
4th rowNew York City
5th rowNew York City
ValueCountFrequency (%)
new21774
25.7%
city21700
25.6%
york21448
25.3%
empty5487
 
6.5%
washington556
 
0.7%
boston443
 
0.5%
brooklyn374
 
0.4%
san352
 
0.4%
philadelphia228
 
0.3%
chicago228
 
0.3%
Other values (1563)12032
14.2%
2025-09-30T03:03:54.289197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45556
 
11.1%
e34669
 
8.4%
t32944
 
8.0%
o30176
 
7.3%
y28566
 
7.0%
i28025
 
6.8%
r27051
 
6.6%
k22936
 
5.6%
C22703
 
5.5%
w22625
 
5.5%
Other values (46)115340
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)410591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45556
 
11.1%
e34669
 
8.4%
t32944
 
8.0%
o30176
 
7.3%
y28566
 
7.0%
i28025
 
6.8%
r27051
 
6.6%
k22936
 
5.6%
C22703
 
5.5%
w22625
 
5.5%
Other values (46)115340
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)410591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45556
 
11.1%
e34669
 
8.4%
t32944
 
8.0%
o30176
 
7.3%
y28566
 
7.0%
i28025
 
6.8%
r27051
 
6.6%
k22936
 
5.6%
C22703
 
5.5%
w22625
 
5.5%
Other values (46)115340
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)410591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45556
 
11.1%
e34669
 
8.4%
t32944
 
8.0%
o30176
 
7.3%
y28566
 
7.0%
i28025
 
6.8%
r27051
 
6.6%
k22936
 
5.6%
C22703
 
5.5%
w22625
 
5.5%
Other values (46)115340
28.1%
Distinct557
Distinct (%)1.4%
Missing99
Missing (%)0.3%
Memory size305.3 KiB
2025-09-30T03:03:54.731905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters389670
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.1%

Sample

1st row2000-08-01
2nd row2011-01-01
3rd row2014-01-01
4th row2018-01-01
5th row2007-10-01
ValueCountFrequency (%)
2018-01-01435
 
1.1%
2019-01-01432
 
1.1%
2020-01-01431
 
1.1%
2022-01-01423
 
1.1%
2017-01-01399
 
1.0%
2021-01-01397
 
1.0%
2021-06-01376
 
1.0%
2022-06-01366
 
0.9%
2016-01-01364
 
0.9%
2019-06-01340
 
0.9%
Other values (547)35004
89.8%
2025-09-30T03:03:55.283628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0120588
30.9%
179514
20.4%
-77934
20.0%
258190
14.9%
911377
 
2.9%
67776
 
2.0%
87650
 
2.0%
57399
 
1.9%
46443
 
1.7%
36429
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)389670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0120588
30.9%
179514
20.4%
-77934
20.0%
258190
14.9%
911377
 
2.9%
67776
 
2.0%
87650
 
2.0%
57399
 
1.9%
46443
 
1.7%
36429
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)389670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0120588
30.9%
179514
20.4%
-77934
20.0%
258190
14.9%
911377
 
2.9%
67776
 
2.0%
87650
 
2.0%
57399
 
1.9%
46443
 
1.7%
36429
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)389670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0120588
30.9%
179514
20.4%
-77934
20.0%
258190
14.9%
911377
 
2.9%
67776
 
2.0%
87650
 
2.0%
57399
 
1.9%
46443
 
1.7%
36429
 
1.6%

ENDDATE
Text

Missing 

Distinct507
Distinct (%)1.6%
Missing7322
Missing (%)18.7%
Memory size305.3 KiB
2025-09-30T03:03:55.712782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters317440
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)0.2%

Sample

1st row2001-06-01
2nd row2014-01-01
3rd row2019-01-01
4th row2021-12-01
5th row2008-09-01
ValueCountFrequency (%)
2018-08-01440
 
1.4%
2019-08-01419
 
1.3%
2017-08-01385
 
1.2%
2022-08-01381
 
1.2%
2021-08-01376
 
1.2%
2019-05-01360
 
1.1%
2021-05-01352
 
1.1%
2023-05-01347
 
1.1%
2022-05-01330
 
1.0%
2023-08-01327
 
1.0%
Other values (497)28027
88.3%
2025-09-30T03:03:56.488625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
096345
30.4%
163668
20.1%
-63488
20.0%
250826
16.0%
87963
 
2.5%
97056
 
2.2%
56878
 
2.2%
45520
 
1.7%
35443
 
1.7%
65261
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)317440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
096345
30.4%
163668
20.1%
-63488
20.0%
250826
16.0%
87963
 
2.5%
97056
 
2.2%
56878
 
2.2%
45520
 
1.7%
35443
 
1.7%
65261
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)317440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
096345
30.4%
163668
20.1%
-63488
20.0%
250826
16.0%
87963
 
2.5%
97056
 
2.2%
56878
 
2.2%
45520
 
1.7%
35443
 
1.7%
65261
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)317440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
096345
30.4%
163668
20.1%
-63488
20.0%
250826
16.0%
87963
 
2.5%
97056
 
2.2%
56878
 
2.2%
45520
 
1.7%
35443
 
1.7%
65261
 
1.7%
Distinct23110
Distinct (%)59.2%
Missing22
Missing (%)0.1%
Memory size305.3 KiB
2025-09-30T03:03:56.822566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length106
Median length92
Mean length26.17913124
Min length2

Characters and Unicode

Total characters1022138
Distinct characters162
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20263 ?
Unique (%)51.9%

Sample

1st rowDeveloper
2nd rowGroup President, Sports Group – Sports Illustrated and Golf
3rd rowBoard Director and Consultant
4th rowDirector, New York Fashion Department
5th rowAssociate Planner - Designer Shoes
ValueCountFrequency (%)
4892
 
3.7%
intern3831
 
2.9%
assistant3512
 
2.7%
manager3232
 
2.4%
director2935
 
2.2%
and2793
 
2.1%
of2580
 
2.0%
associate2530
 
1.9%
senior2321
 
1.8%
research2106
 
1.6%
Other values (9667)101305
76.7%
2025-09-30T03:03:57.457738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e102313
 
10.0%
93151
 
9.1%
r75563
 
7.4%
t74951
 
7.3%
n74550
 
7.3%
a69683
 
6.8%
i67911
 
6.6%
o55920
 
5.5%
s52062
 
5.1%
c36487
 
3.6%
Other values (152)319547
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1022138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e102313
 
10.0%
93151
 
9.1%
r75563
 
7.4%
t74951
 
7.3%
n74550
 
7.3%
a69683
 
6.8%
i67911
 
6.6%
o55920
 
5.5%
s52062
 
5.1%
c36487
 
3.6%
Other values (152)319547
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1022138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e102313
 
10.0%
93151
 
9.1%
r75563
 
7.4%
t74951
 
7.3%
n74550
 
7.3%
a69683
 
6.8%
i67911
 
6.6%
o55920
 
5.5%
s52062
 
5.1%
c36487
 
3.6%
Other values (152)319547
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1022138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e102313
 
10.0%
93151
 
9.1%
r75563
 
7.4%
t74951
 
7.3%
n74550
 
7.3%
a69683
 
6.8%
i67911
 
6.6%
o55920
 
5.5%
s52062
 
5.1%
c36487
 
3.6%
Other values (152)319547
31.3%
Distinct1222
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:57.840113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length11.82864895
Min length1

Characters and Unicode

Total characters462098
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)0.5%

Sample

1st rowdeveloper
2nd rowadvertising
3rd rowstrategic
4th rownanny
5th rowmerchandising
ValueCountFrequency (%)
research3080
 
5.4%
analyst2058
 
3.6%
marketing1582
 
2.8%
teaching1424
 
2.5%
social1323
 
2.3%
teacher1258
 
2.2%
scientist1095
 
1.9%
engineer1081
 
1.9%
media1009
 
1.8%
writer986
 
1.7%
Other values (564)41931
73.8%
2025-09-30T03:03:58.517799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e54769
11.9%
t42303
9.2%
r41454
 
9.0%
a41019
 
8.9%
i37180
 
8.0%
s34689
 
7.5%
n34266
 
7.4%
c28674
 
6.2%
o25441
 
5.5%
17761
 
3.8%
Other values (17)104542
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)462098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e54769
11.9%
t42303
9.2%
r41454
 
9.0%
a41019
 
8.9%
i37180
 
8.0%
s34689
 
7.5%
n34266
 
7.4%
c28674
 
6.2%
o25441
 
5.5%
17761
 
3.8%
Other values (17)104542
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)462098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e54769
11.9%
t42303
9.2%
r41454
 
9.0%
a41019
 
8.9%
i37180
 
8.0%
s34689
 
7.5%
n34266
 
7.4%
c28674
 
6.2%
o25441
 
5.5%
17761
 
3.8%
Other values (17)104542
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)462098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e54769
11.9%
t42303
9.2%
r41454
 
9.0%
a41019
 
8.9%
i37180
 
8.0%
s34689
 
7.5%
n34266
 
7.4%
c28674
 
6.2%
o25441
 
5.5%
17761
 
3.8%
Other values (17)104542
22.6%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:03:58.722336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.353785901
Min length5

Characters and Unicode

Total characters287283
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngineer
2nd rowMarketing
3rd rowSales
4th rowSales
5th rowSales
ValueCountFrequency (%)
marketing10365
26.5%
admin8040
20.6%
scientist5528
14.2%
sales5309
13.6%
engineer4452
11.4%
finance3945
 
10.1%
operations1427
 
3.7%
2025-09-30T03:03:59.080878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n42154
14.7%
i39285
13.7%
e35478
12.3%
t22848
 
8.0%
a21046
 
7.3%
r16244
 
5.7%
g14817
 
5.2%
s12264
 
4.3%
S10837
 
3.8%
M10365
 
3.6%
Other values (11)61945
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)287283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n42154
14.7%
i39285
13.7%
e35478
12.3%
t22848
 
8.0%
a21046
 
7.3%
r16244
 
5.7%
g14817
 
5.2%
s12264
 
4.3%
S10837
 
3.8%
M10365
 
3.6%
Other values (11)61945
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)287283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n42154
14.7%
i39285
13.7%
e35478
12.3%
t22848
 
8.0%
a21046
 
7.3%
r16244
 
5.7%
g14817
 
5.2%
s12264
 
4.3%
S10837
 
3.8%
M10365
 
3.6%
Other values (11)61945
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)287283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n42154
14.7%
i39285
13.7%
e35478
12.3%
t22848
 
8.0%
a21046
 
7.3%
r16244
 
5.7%
g14817
 
5.2%
s12264
 
4.3%
S10837
 
3.8%
M10365
 
3.6%
Other values (11)61945
21.6%

DESCRIPTION
Text

Missing 

Distinct22723
Distinct (%)99.0%
Missing16102
Missing (%)41.2%
Memory size305.3 KiB
2025-09-30T03:03:59.454527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2063
Median length1636
Mean length402.0821721
Min length1

Characters and Unicode

Total characters9233415
Distinct characters188
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22518 ?
Unique (%)98.1%

Sample

1st rowHTML, javascript, DHTML development Sole front-end developer for the following Primix clients: 1. EMC Corporation• On site development for the EMC Sales and Marketing intranet site• Extensive utilization of HTML, JavaScript, HomeSite, Photoshop, for Sales and Marketing intranet site• Introduced to the uses of XML, SourceSafe, and Documentum for content conversion 2. PitneyWorks, division of Pitney Bowes• Developed the front-end of then online Mailing and Shipping Tool: Browser Click Stamp Online• Supplied a click-through prototype to the client to be used for user-testing. Observed this user-testing on site and worked closely with the client to make real time updates to the prototype based on user feedback• Created templates from wireframes, applying look & feel, and used them to produce 150+ pages for actual site• Successfully completed a cross-browser DHTML page in which form elements appear and disappear and content dynamically changes depending on users’ initial choices 3. Depuy Acromed, division of Johnson and Johnson• Designed and implemented a 5-page clickable demo tailored for the uses of their Sales Representatives
2nd rowOversaw global business/operations development for $600M sports group, with a digitalize/innovate mandate by CEO and Board.• Launched Sports Illustrated China.• Expanded Swimsuit into a stand-alone business reaching more adults than the Super Bowl via live-streaming and videos.• Relaunched Sports Illustrated.com to deliver video content through mobile-first platform; added live talk show and digital subscriptions; plus partnered with MLB and NHL to launch digital sports network under SI.com.
3rd rowValassis Communications, which is one of the world's largest coupon distributors/processors, is a a former public company taken private in 2014 when acquired as a wholly owned subsidiary of M&F Worldwide/MacAndrews & Forbes, Inc., a U.S. diversified holding firm. Of note includes:• Suggested transition from newspaper free-standing inserts (FSIs) company to a marketing technology and omnichannel solutions firm that predicts behavior and spurs activation with direct mail, display ads, and mobile.
4th rowEarned five promotions from 1985 to 1997 at Time; from starting position at J. Walter Thompson, progressively promoted into more senior roles at Hearst Corporation, including named Midwestern Manager for Cosmopolitan.
5th rowFor this social media optimization platform that creates new revenue streams for publishers:• Introduced company to publishers with content distribution services that amplified their social media presence, resulting in new revenue opportunities that led to acquisition by Piano, a digital experience manufacturer.
ValueCountFrequency (%)
and81983
 
6.4%
the37717
 
2.9%
to30870
 
2.4%
of30171
 
2.4%
for23880
 
1.9%
in21742
 
1.7%
a16759
 
1.3%
with15245
 
1.2%
13624
 
1.1%
•11894
 
0.9%
Other values (72416)996890
77.8%
2025-09-30T03:04:00.226150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1253842
13.6%
e851162
 
9.2%
n623065
 
6.7%
a615162
 
6.7%
t609241
 
6.6%
i598877
 
6.5%
o529462
 
5.7%
r494842
 
5.4%
s492686
 
5.3%
d328168
 
3.6%
Other values (178)2836908
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)9233415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1253842
13.6%
e851162
 
9.2%
n623065
 
6.7%
a615162
 
6.7%
t609241
 
6.6%
i598877
 
6.5%
o529462
 
5.7%
r494842
 
5.4%
s492686
 
5.3%
d328168
 
3.6%
Other values (178)2836908
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9233415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1253842
13.6%
e851162
 
9.2%
n623065
 
6.7%
a615162
 
6.7%
t609241
 
6.6%
i598877
 
6.5%
o529462
 
5.7%
r494842
 
5.4%
s492686
 
5.3%
d328168
 
3.6%
Other values (178)2836908
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9233415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1253842
13.6%
e851162
 
9.2%
n623065
 
6.7%
a615162
 
6.7%
t609241
 
6.6%
i598877
 
6.5%
o529462
 
5.7%
r494842
 
5.4%
s492686
 
5.3%
d328168
 
3.6%
Other values (178)2836908
30.7%
Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:00.552378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length12.75853684
Min length5

Characters and Unicode

Total characters498425
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoftware Engineer
2nd rowMarketing
3rd rowProduct Manager
4th rowCashier
5th rowMerchandiser
ValueCountFrequency (%)
producer5591
 
9.7%
coordinator5291
 
9.2%
specialist4716
 
8.2%
scientist3070
 
5.3%
manager2674
 
4.7%
medical2229
 
3.9%
rep2229
 
3.9%
engineer2094
 
3.6%
investment1957
 
3.4%
sales1781
 
3.1%
Other values (55)25782
44.9%
2025-09-30T03:04:01.060426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e53467
 
10.7%
i44446
 
8.9%
r42367
 
8.5%
a38053
 
7.6%
t37727
 
7.6%
n35491
 
7.1%
o34567
 
6.9%
c28262
 
5.7%
s24851
 
5.0%
18348
 
3.7%
Other values (31)140846
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)498425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e53467
 
10.7%
i44446
 
8.9%
r42367
 
8.5%
a38053
 
7.6%
t37727
 
7.6%
n35491
 
7.1%
o34567
 
6.9%
c28262
 
5.7%
s24851
 
5.0%
18348
 
3.7%
Other values (31)140846
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)498425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e53467
 
10.7%
i44446
 
8.9%
r42367
 
8.5%
a38053
 
7.6%
t37727
 
7.6%
n35491
 
7.1%
o34567
 
6.9%
c28262
 
5.7%
s24851
 
5.0%
18348
 
3.7%
Other values (31)140846
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)498425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e53467
 
10.7%
i44446
 
8.9%
r42367
 
8.5%
a38053
 
7.6%
t37727
 
7.6%
n35491
 
7.1%
o34567
 
6.9%
c28262
 
5.7%
s24851
 
5.0%
18348
 
3.7%
Other values (31)140846
28.3%
Distinct149
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:01.403664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length13.36476732
Min length2

Characters and Unicode

Total characters522108
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSoftware Developer
2nd rowMarketing
3rd rowCorporate Strategy
4th rowCleaner
5th rowMerchandiser
ValueCountFrequency (%)
specialist5051
 
7.9%
corporate4546
 
7.1%
trainer3658
 
5.7%
manager3422
 
5.3%
scientist3372
 
5.3%
writer2385
 
3.7%
producer1935
 
3.0%
designer1694
 
2.6%
legal1635
 
2.6%
engineer1444
 
2.3%
Other values (145)34945
54.5%
2025-09-30T03:04:02.077731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e61649
11.8%
r47973
 
9.2%
i44517
 
8.5%
t44118
 
8.4%
a42948
 
8.2%
n36498
 
7.0%
o26319
 
5.0%
c25034
 
4.8%
25021
 
4.8%
s24014
 
4.6%
Other values (37)144017
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)522108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e61649
11.8%
r47973
 
9.2%
i44517
 
8.5%
t44118
 
8.4%
a42948
 
8.2%
n36498
 
7.0%
o26319
 
5.0%
c25034
 
4.8%
25021
 
4.8%
s24014
 
4.6%
Other values (37)144017
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)522108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e61649
11.8%
r47973
 
9.2%
i44517
 
8.5%
t44118
 
8.4%
a42948
 
8.2%
n36498
 
7.0%
o26319
 
5.0%
c25034
 
4.8%
25021
 
4.8%
s24014
 
4.6%
Other values (37)144017
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)522108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e61649
11.8%
r47973
 
9.2%
i44517
 
8.5%
t44118
 
8.4%
a42948
 
8.2%
n36498
 
7.0%
o26319
 
5.0%
c25034
 
4.8%
25021
 
4.8%
s24014
 
4.6%
Other values (37)144017
27.6%
Distinct289
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:02.755359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length13.67823683
Min length2

Characters and Unicode

Total characters534354
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowSoftware Developer
2nd rowMarketing
3rd rowEngagement Manager
4th rowHome Department Manager
5th rowMerchandiser
ValueCountFrequency (%)
specialist4754
 
7.3%
corporate4234
 
6.5%
trainer3659
 
5.6%
scientist3104
 
4.8%
manager2699
 
4.1%
writer2385
 
3.7%
designer1694
 
2.6%
legal1591
 
2.4%
engineer1391
 
2.1%
content1331
 
2.0%
Other values (249)38325
58.8%
2025-09-30T03:04:04.388857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e60196
 
11.3%
r46941
 
8.8%
t46636
 
8.7%
i45969
 
8.6%
a41851
 
7.8%
n39556
 
7.4%
o29258
 
5.5%
26101
 
4.9%
s25295
 
4.7%
c21396
 
4.0%
Other values (42)151155
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)534354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e60196
 
11.3%
r46941
 
8.8%
t46636
 
8.7%
i45969
 
8.6%
a41851
 
7.8%
n39556
 
7.4%
o29258
 
5.5%
26101
 
4.9%
s25295
 
4.7%
c21396
 
4.0%
Other values (42)151155
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)534354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e60196
 
11.3%
r46941
 
8.8%
t46636
 
8.7%
i45969
 
8.6%
a41851
 
7.8%
n39556
 
7.4%
o29258
 
5.5%
26101
 
4.9%
s25295
 
4.7%
c21396
 
4.0%
Other values (42)151155
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)534354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e60196
 
11.3%
r46941
 
8.8%
t46636
 
8.7%
i45969
 
8.6%
a41851
 
7.8%
n39556
 
7.4%
o29258
 
5.5%
26101
 
4.9%
s25295
 
4.7%
c21396
 
4.0%
Other values (42)151155
28.3%
Distinct459
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:04.912851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length27
Mean length13.21056673
Min length2

Characters and Unicode

Total characters516084
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st rowSoftware Developer
2nd rowMarketing
3rd rowEngagement Manager
4th rowHome Department Manager
5th rowMerchandiser
ValueCountFrequency (%)
specialist4616
 
7.3%
manager3182
 
5.0%
scientist3058
 
4.8%
writer2341
 
3.7%
professor2062
 
3.2%
designer1791
 
2.8%
legal1591
 
2.5%
teacher1546
 
2.4%
analyst1336
 
2.1%
engineer1272
 
2.0%
Other values (354)40871
64.2%
2025-09-30T03:04:05.592056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e59392
 
11.5%
i42609
 
8.3%
a40717
 
7.9%
t40572
 
7.9%
r40323
 
7.8%
n35628
 
6.9%
s30391
 
5.9%
24600
 
4.8%
o24496
 
4.7%
c22788
 
4.4%
Other values (45)154568
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)516084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e59392
 
11.5%
i42609
 
8.3%
a40717
 
7.9%
t40572
 
7.9%
r40323
 
7.8%
n35628
 
6.9%
s30391
 
5.9%
24600
 
4.8%
o24496
 
4.7%
c22788
 
4.4%
Other values (45)154568
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)516084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e59392
 
11.5%
i42609
 
8.3%
a40717
 
7.9%
t40572
 
7.9%
r40323
 
7.8%
n35628
 
6.9%
s30391
 
5.9%
24600
 
4.8%
o24496
 
4.7%
c22788
 
4.4%
Other values (45)154568
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)516084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e59392
 
11.5%
i42609
 
8.3%
a40717
 
7.9%
t40572
 
7.9%
r40323
 
7.8%
n35628
 
6.9%
s30391
 
5.9%
24600
 
4.8%
o24496
 
4.7%
c22788
 
4.4%
Other values (45)154568
30.0%
Distinct866
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:06.016965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length13.76432192
Min length2

Characters and Unicode

Total characters537717
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)0.3%

Sample

1st rowDeveloper
2nd rowAdvertising Specialist
3rd rowStrategic Projects
4th rowNanny
5th rowMerchandising Specialist
ValueCountFrequency (%)
specialist4872
 
7.4%
professor2062
 
3.1%
researcher1817
 
2.8%
manager1605
 
2.4%
analyst1510
 
2.3%
editor1482
 
2.2%
social1323
 
2.0%
marketing1283
 
1.9%
teacher1258
 
1.9%
scientist1204
 
1.8%
Other values (525)47654
72.1%
2025-09-30T03:04:06.739754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e60557
 
11.3%
r44066
 
8.2%
i43378
 
8.1%
t40398
 
7.5%
a40221
 
7.5%
n33208
 
6.2%
s32667
 
6.1%
o29680
 
5.5%
27004
 
5.0%
c24770
 
4.6%
Other values (45)161768
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)537717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e60557
 
11.3%
r44066
 
8.2%
i43378
 
8.1%
t40398
 
7.5%
a40221
 
7.5%
n33208
 
6.2%
s32667
 
6.1%
o29680
 
5.5%
27004
 
5.0%
c24770
 
4.6%
Other values (45)161768
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)537717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e60557
 
11.3%
r44066
 
8.2%
i43378
 
8.1%
t40398
 
7.5%
a40221
 
7.5%
n33208
 
6.2%
s32667
 
6.1%
o29680
 
5.5%
27004
 
5.0%
c24770
 
4.6%
Other values (45)161768
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)537717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e60557
 
11.3%
r44066
 
8.2%
i43378
 
8.1%
t40398
 
7.5%
a40221
 
7.5%
n33208
 
6.2%
s32667
 
6.1%
o29680
 
5.5%
27004
 
5.0%
c24770
 
4.6%
Other values (45)161768
30.1%

SENIORITY
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.670659909
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:07.076959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.708358101
Coefficient of variation (CV)0.6396763945
Kurtosis-0.6966211461
Mean2.670659909
Median Absolute Deviation (MAD)1
Skewness0.7290899805
Sum104332
Variance2.918487403
MonotonicityNot monotonic
2025-09-30T03:04:07.235632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
113527
34.6%
29375
24.0%
55389
 
13.8%
34135
 
10.6%
43899
 
10.0%
62078
 
5.3%
7663
 
1.7%
ValueCountFrequency (%)
113527
34.6%
29375
24.0%
34135
 
10.6%
43899
 
10.0%
55389
 
13.8%
ValueCountFrequency (%)
7663
 
1.7%
62078
 
5.3%
55389
13.8%
43899
10.0%
34135
10.6%

SALARY
Real number (ℝ)

Distinct39033
Distinct (%)99.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean96510.9335
Minimum1577.443604
Maximum985071.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:07.438721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1577.443604
5-th percentile23773.10361
Q144396.36914
median70864.98438
Q3119456.3691
95-th percentile264304.9297
Maximum985071.125
Range983493.6814
Interquartile range (IQR)75060

Descriptive statistics

Standard deviation78920.29009
Coefficient of variation (CV)0.8177341906
Kurtosis6.16928325
Mean96510.9335
Median Absolute Deviation (MAD)31886.21875
Skewness2.065322752
Sum3770103106
Variance6228412188
MonotonicityNot monotonic
2025-09-30T03:04:07.666578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
378749.3754
 
< 0.1%
357500.3754
 
< 0.1%
59008.402342
 
< 0.1%
52198.277342
 
< 0.1%
121656.19532
 
< 0.1%
6800002
 
< 0.1%
37776.425782
 
< 0.1%
44745.855472
 
< 0.1%
89217.617192
 
< 0.1%
75779.601562
 
< 0.1%
Other values (39023)39040
99.9%
ValueCountFrequency (%)
1577.4436041
< 0.1%
1659.379151
< 0.1%
1678.7307131
< 0.1%
1849.3812261
< 0.1%
1900.1182861
< 0.1%
ValueCountFrequency (%)
985071.1251
< 0.1%
851264.6251
< 0.1%
827263.81251
< 0.1%
807045.81251
< 0.1%
789477.6251
< 0.1%

TOTAL_COMPENSATION
Real number (ℝ)

Distinct39034
Distinct (%)99.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean134012.1471
Minimum1938.810181
Maximum3768033.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:08.233741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1938.810181
5-th percentile26731.78613
Q150133.2666
median81017.15625
Q3145515.9492
95-th percentile411008.6531
Maximum3768033.75
Range3766094.94
Interquartile range (IQR)95382.68262

Descriptive statistics

Standard deviation170557.9476
Coefficient of variation (CV)1.272705134
Kurtosis47.26913979
Mean134012.1471
Median Absolute Deviation (MAD)37946.31055
Skewness5.20823021
Sum5235050514
Variance2.90900135 × 1010
MonotonicityNot monotonic
2025-09-30T03:04:08.446813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66289.148442
 
< 0.1%
84140.398442
 
< 0.1%
79736.898442
 
< 0.1%
88759.398442
 
< 0.1%
40999.921882
 
< 0.1%
43934.652342
 
< 0.1%
38622.195312
 
< 0.1%
60422.140622
 
< 0.1%
39968.496092
 
< 0.1%
46030.746092
 
< 0.1%
Other values (39024)39044
99.9%
ValueCountFrequency (%)
1938.8101811
< 0.1%
2171.4426271
< 0.1%
2198.8208011
< 0.1%
2254.5061041
< 0.1%
2267.9902341
< 0.1%
ValueCountFrequency (%)
3768033.751
< 0.1%
37331071
< 0.1%
3684005.751
< 0.1%
2683586.51
< 0.1%
2421338.751
< 0.1%

PRESTIGE
Real number (ℝ)

Zeros 

Distinct38356
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4153518502
Minimum-0.9719650416
Maximum1
Zeros682
Zeros (%)1.7%
Negative7613
Negative (%)19.5%
Memory size305.3 KiB
2025-09-30T03:04:08.660985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9719650416
5-th percentile-0.4826184058
Q10.1124327944
median0.545473012
Q30.7773640347
95-th percentile0.935618379
Maximum1
Range1.971965042
Interquartile range (IQR)0.6649312403

Descriptive statistics

Standard deviation0.4450694144
Coefficient of variation (CV)1.071547928
Kurtosis-0.3831481846
Mean0.4153518502
Median Absolute Deviation (MAD)0.2835350207
Skewness-0.8048343648
Sum16226.13538
Variance0.1980867836
MonotonicityNot monotonic
2025-09-30T03:04:08.876932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0682
 
1.7%
0.81619912779
 
< 0.1%
-0.0668046
 
< 0.1%
16
 
< 0.1%
0.92818985646
 
< 0.1%
0.8319822
 
< 0.1%
-0.17786416792
 
< 0.1%
0.92951543352
 
< 0.1%
0.82877892262
 
< 0.1%
0.1284122
 
< 0.1%
Other values (38346)38347
98.2%
ValueCountFrequency (%)
-0.97196504161
< 0.1%
-0.9710481
< 0.1%
-0.935531
< 0.1%
-0.92864347731
< 0.1%
-0.92620990791
< 0.1%
ValueCountFrequency (%)
16
< 0.1%
0.99200778741
 
< 0.1%
0.99185221791
 
< 0.1%
0.9917229261
 
< 0.1%
0.99153165161
 
< 0.1%

RN
Real number (ℝ)

Distinct143
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.934009113
Minimum1
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:09.084296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile17
Maximum143
Range142
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.50625819
Coefficient of variation (CV)1.226744593
Kurtosis79.52171778
Mean6.934009113
Median Absolute Deviation (MAD)3
Skewness7.123659361
Sum270884
Variance72.3564284
MonotonicityNot monotonic
2025-09-30T03:04:09.283038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14655
11.9%
24407
11.3%
34220
10.8%
43957
10.1%
53606
9.2%
63195
8.2%
72806
7.2%
82331
 
6.0%
91896
 
4.9%
101502
 
3.8%
Other values (133)6491
16.6%
ValueCountFrequency (%)
14655
11.9%
24407
11.3%
34220
10.8%
43957
10.1%
53606
9.2%
ValueCountFrequency (%)
1431
< 0.1%
1421
< 0.1%
1411
< 0.1%
1401
< 0.1%
1391
< 0.1%

RCID
Real number (ℝ)

Missing 

Distinct16011
Distinct (%)47.0%
Missing5016
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean7853503.374
Minimum6
Maximum102380922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:09.499056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile103863.1
Q1550201.75
median1104097
Q35326825.75
95-th percentile24829039
Maximum102380922
Range102380916
Interquartile range (IQR)4776624

Descriptive statistics

Standard deviation19484498.68
Coefficient of variation (CV)2.4809945
Kurtosis14.27939458
Mean7853503.374
Median Absolute Deviation (MAD)717472
Skewness3.833590179
Sum2.674117899 × 1011
Variance3.796456887 × 1014
MonotonicityNot monotonic
2025-09-30T03:04:09.712339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676991240
 
0.6%
1165410208
 
0.5%
22142390167
 
0.4%
20923715146
 
0.4%
15768159145
 
0.4%
1304181133
 
0.3%
709929129
 
0.3%
267301122
 
0.3%
20937770122
 
0.3%
1485785108
 
0.3%
Other values (16001)32530
83.3%
(Missing)5016
 
12.8%
ValueCountFrequency (%)
61
 
< 0.1%
21816
< 0.1%
3501
 
< 0.1%
4051
 
< 0.1%
4072
 
< 0.1%
ValueCountFrequency (%)
1023809221
< 0.1%
1023801571
< 0.1%
1022643851
< 0.1%
1022525431
< 0.1%
1022454241
< 0.1%

COMPANY_NAME
Text

Missing 

Distinct16009
Distinct (%)47.0%
Missing5022
Missing (%)12.9%
Memory size305.3 KiB
2025-09-30T03:04:10.058839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length92
Median length73
Mean length23.94615791
Min length2

Characters and Unicode

Total characters815223
Distinct characters155
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11521 ?
Unique (%)33.8%

Sample

1st rowPrimix Solutions, Inc.
2nd rowTI Gotham, Inc.
3rd rowValassis Sales & Marketing Services, Inc.
4th rowPsone Art Education
5th rowMacy's, Inc.
ValueCountFrequency (%)
inc8632
 
7.1%
of4508
 
3.7%
university3707
 
3.0%
llc3306
 
2.7%
the3212
 
2.6%
2559
 
2.1%
new2364
 
1.9%
york1921
 
1.6%
ltd1489
 
1.2%
group1419
 
1.2%
Other values (14986)88831
72.8%
2025-09-30T03:04:10.629772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87908
 
10.8%
e65174
 
8.0%
n53742
 
6.6%
o48320
 
5.9%
i47090
 
5.8%
a43620
 
5.4%
t43618
 
5.4%
r42418
 
5.2%
s31738
 
3.9%
l27296
 
3.3%
Other values (145)324299
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)815223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87908
 
10.8%
e65174
 
8.0%
n53742
 
6.6%
o48320
 
5.9%
i47090
 
5.8%
a43620
 
5.4%
t43618
 
5.4%
r42418
 
5.2%
s31738
 
3.9%
l27296
 
3.3%
Other values (145)324299
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)815223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87908
 
10.8%
e65174
 
8.0%
n53742
 
6.6%
o48320
 
5.9%
i47090
 
5.8%
a43620
 
5.4%
t43618
 
5.4%
r42418
 
5.2%
s31738
 
3.9%
l27296
 
3.3%
Other values (145)324299
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)815223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87908
 
10.8%
e65174
 
8.0%
n53742
 
6.6%
o48320
 
5.9%
i47090
 
5.8%
a43620
 
5.4%
t43618
 
5.4%
r42418
 
5.2%
s31738
 
3.9%
l27296
 
3.3%
Other values (145)324299
39.8%

ULTIMATE_PARENT_RCID
Real number (ℝ)

Missing 

Distinct14146
Distinct (%)41.5%
Missing5017
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean9960777.46
Minimum6
Maximum102380922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:10.845902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile121548.6
Q1593124
median1127719
Q36922141
95-th percentile93104739
Maximum102380922
Range102380916
Interquartile range (IQR)6329017

Descriptive statistics

Standard deviation22512565.69
Coefficient of variation (CV)2.260121339
Kurtosis9.626682424
Mean9960777.46
Median Absolute Deviation (MAD)769130
Skewness3.234333932
Sum3.391545117 × 1011
Variance5.068156141 × 1014
MonotonicityNot monotonic
2025-09-30T03:04:11.053859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006288439
 
1.1%
676991421
 
1.1%
1165410283
 
0.7%
543448199
 
0.5%
476155190
 
0.5%
102944176
 
0.5%
22142390174
 
0.4%
1237807170
 
0.4%
786030162
 
0.4%
329436161
 
0.4%
Other values (14136)31674
81.1%
(Missing)5017
 
12.8%
ValueCountFrequency (%)
61
 
< 0.1%
21820
0.1%
4051
 
< 0.1%
11921
 
< 0.1%
13703
 
< 0.1%
ValueCountFrequency (%)
1023809221
 
< 0.1%
1023801571
 
< 0.1%
1023709106
 
< 0.1%
1023601721
 
< 0.1%
10232704719
< 0.1%
Distinct14144
Distinct (%)41.5%
Missing5023
Missing (%)12.9%
Memory size305.3 KiB
2025-09-30T03:04:11.440680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length92
Median length73
Mean length23.67761361
Min length2

Characters and Unicode

Total characters806057
Distinct characters154
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10202 ?
Unique (%)30.0%

Sample

1st rowDeutsche Telekom AG
2nd rowIAC, Inc.
3rd rowRRD Intermediate Holdings, Inc.
4th rowPsone Art Education
5th rowMacy's, Inc.
ValueCountFrequency (%)
inc8722
 
7.2%
of5689
 
4.7%
university4183
 
3.4%
the4139
 
3.4%
new2897
 
2.4%
llc2436
 
2.0%
york2433
 
2.0%
2313
 
1.9%
group1917
 
1.6%
corp1895
 
1.6%
Other values (14051)85357
70.0%
2025-09-30T03:04:12.049602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87949
 
10.9%
e63681
 
7.9%
n53024
 
6.6%
o49130
 
6.1%
i46059
 
5.7%
t43576
 
5.4%
r42834
 
5.3%
a41921
 
5.2%
s31377
 
3.9%
l25837
 
3.2%
Other values (144)320669
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)806057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87949
 
10.9%
e63681
 
7.9%
n53024
 
6.6%
o49130
 
6.1%
i46059
 
5.7%
t43576
 
5.4%
r42834
 
5.3%
a41921
 
5.2%
s31377
 
3.9%
l25837
 
3.2%
Other values (144)320669
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)806057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87949
 
10.9%
e63681
 
7.9%
n53024
 
6.6%
o49130
 
6.1%
i46059
 
5.7%
t43576
 
5.4%
r42834
 
5.3%
a41921
 
5.2%
s31377
 
3.9%
l25837
 
3.2%
Other values (144)320669
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)806057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87949
 
10.9%
e63681
 
7.9%
n53024
 
6.6%
o49130
 
6.1%
i46059
 
5.7%
t43576
 
5.4%
r42834
 
5.3%
a41921
 
5.2%
s31377
 
3.9%
l25837
 
3.2%
Other values (144)320669
39.8%

WEIGHT
Real number (ℝ)

Skewed 

Distinct4526
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.045517774
Minimum1
Maximum9.098433495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.3 KiB
2025-09-30T03:04:12.250232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.000833273
Q11.009209991
median1.021778822
Q31.044798017
95-th percentile1.139070511
Maximum9.098433495
Range8.098433495
Interquartile range (IQR)0.035588026

Descriptive statistics

Standard deviation0.1625135054
Coefficient of variation (CV)0.1554383
Kurtosis1306.139824
Mean1.045517774
Median Absolute Deviation (MAD)0.015255928
Skewness31.0129104
Sum40844.19737
Variance0.02641063944
MonotonicityNot monotonic
2025-09-30T03:04:12.459324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.000783324486
 
1.2%
1.025333166143
 
0.4%
1.060254812134
 
0.3%
1.0007659290
 
0.2%
1.02762472665
 
0.2%
1.01368904156
 
0.1%
1.01795327754
 
0.1%
1.01609122853
 
0.1%
1.02505171353
 
0.1%
1.08338642153
 
0.1%
Other values (4516)37879
97.0%
ValueCountFrequency (%)
138
0.1%
1.0000189541
 
< 0.1%
1.0000200272
 
< 0.1%
1.0000298027
 
< 0.1%
1.0000447039
 
< 0.1%
ValueCountFrequency (%)
9.0984334957
< 0.1%
6.0238275535
< 0.1%
5.169245724
< 0.1%
4.9524464614
< 0.1%
4.8789625174
< 0.1%

RICS_K50
Text

Missing 

Distinct50
Distinct (%)0.1%
Missing5020
Missing (%)12.9%
Memory size305.3 KiB
2025-09-30T03:04:12.771248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length23.97559185
Min length13

Characters and Unicode

Total characters816273
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInformation Technology Services
2nd rowMedia and Entertainment
3rd rowMarketing and Advertising Services
4th rowEducation Services
5th rowRetail and Consumer Goods
ValueCountFrequency (%)
services22871
24.0%
and13439
14.1%
education5737
 
6.0%
entertainment3880
 
4.1%
financial3513
 
3.7%
healthcare3102
 
3.3%
wellness3068
 
3.2%
media2991
 
3.1%
technology2423
 
2.5%
information2414
 
2.5%
Other values (66)31718
33.3%
2025-09-30T03:04:13.226142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e102383
12.5%
n69895
 
8.6%
i67596
 
8.3%
a62621
 
7.7%
61110
 
7.5%
r49646
 
6.1%
c47096
 
5.8%
s44979
 
5.5%
t44093
 
5.4%
o32603
 
4.0%
Other values (29)234251
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)816273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e102383
12.5%
n69895
 
8.6%
i67596
 
8.3%
a62621
 
7.7%
61110
 
7.5%
r49646
 
6.1%
c47096
 
5.8%
s44979
 
5.5%
t44093
 
5.4%
o32603
 
4.0%
Other values (29)234251
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)816273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e102383
12.5%
n69895
 
8.6%
i67596
 
8.3%
a62621
 
7.7%
61110
 
7.5%
r49646
 
6.1%
c47096
 
5.8%
s44979
 
5.5%
t44093
 
5.4%
o32603
 
4.0%
Other values (29)234251
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)816273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e102383
12.5%
n69895
 
8.6%
i67596
 
8.3%
a62621
 
7.7%
61110
 
7.5%
r49646
 
6.1%
c47096
 
5.8%
s44979
 
5.5%
t44093
 
5.4%
o32603
 
4.0%
Other values (29)234251
28.7%

RICS_K400
Text

Missing 

Distinct395
Distinct (%)1.2%
Missing5020
Missing (%)12.9%
Memory size305.3 KiB
2025-09-30T03:04:13.563681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length51
Mean length30.31504435
Min length6

Characters and Unicode

Total characters1032106
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowEnterprise Software and IT Services
2nd rowMedia and Entertainment
3rd rowMarketing and Advertising Services
4th rowEducational Institutions
5th rowRetail and Consumer Goods
ValueCountFrequency (%)
and25701
 
20.3%
services13524
 
10.7%
research4628
 
3.7%
universities4283
 
3.4%
media2669
 
2.1%
management2365
 
1.9%
financial2096
 
1.7%
development1945
 
1.5%
education1899
 
1.5%
marketing1893
 
1.5%
Other values (320)65467
51.8%
2025-09-30T03:04:14.128930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e114113
 
11.1%
n94062
 
9.1%
92424
 
9.0%
a86694
 
8.4%
i86096
 
8.3%
s64038
 
6.2%
t59517
 
5.8%
r55939
 
5.4%
c45073
 
4.4%
d36171
 
3.5%
Other values (40)297979
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1032106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e114113
 
11.1%
n94062
 
9.1%
92424
 
9.0%
a86694
 
8.4%
i86096
 
8.3%
s64038
 
6.2%
t59517
 
5.8%
r55939
 
5.4%
c45073
 
4.4%
d36171
 
3.5%
Other values (40)297979
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1032106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e114113
 
11.1%
n94062
 
9.1%
92424
 
9.0%
a86694
 
8.4%
i86096
 
8.3%
s64038
 
6.2%
t59517
 
5.8%
r55939
 
5.4%
c45073
 
4.4%
d36171
 
3.5%
Other values (40)297979
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1032106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e114113
 
11.1%
n94062
 
9.1%
92424
 
9.0%
a86694
 
8.4%
i86096
 
8.3%
s64038
 
6.2%
t59517
 
5.8%
r55939
 
5.4%
c45073
 
4.4%
d36171
 
3.5%
Other values (40)297979
28.9%